Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1, 4-12 and 14-23 are pending in this office action.
Claims 2-3 and 13 are cancelled.
Claims 21-23 are new added claims.
Response to Arguments
Applicant's arguments filed 07/24/2026 have been fully considered but they are not persuasive.
Applicant’s argument:
When addressing the features of previously presented claim 2, the Office Action at page 12 alleges that Rieken's descriptions at paragraphs [0038] and [0048] teach the previously recited claimed feature of "wherein the first input parameter is identified based on a ranking score and/or a type of the identified issue." While not conceding to the appropriateness of the Office Action's allegations regarding the cited references, Applicant respectfully submits that claim 1 has been amended to recite "wherein the first input parameter is identified based on a ranking score." As best understood, Rieken teaches "marking of a warning or an error in which code may include underlining a portion of the code" and "generate error information 550 to indicate (e.g., specify or describe) the programming error." Therefore, Rieken fails to disclose, teach or suggest "wherein the first input parameter is identified based on a ranking score" as claimed. Thus, this feature is a distinction over the cited references.
Examiner’s response:
Claim 2 as support for the amendment has a set o choices to select. And as applicant’s representative acknowledged above the error description/category/type is disclosed in the prior arts. Now the applicant’s representative restricts the set to only: “ranking score” that different that and/or in the previous claim.
First, what is the ranking score used in this instant application?
[0062] “The input parameter(s) 201 may be modified based on a ranking score and/or a type of the issue. For example, if the type of issue is a critical type of malware, the input parameter 201 may be automatically modified. For issues that are less critical, a threshold may have to be met (e.g., a number of less critical issues). The new input parameter(s) 201 are then input into the code AI algorithm 124 in step 506.
The criticality of the issue may make at least 2 buckets: critical issue and less/not critical issue. And the input parameters may fall in either bucket.
Back to the arts:
Hawker discloses:
[0055] “If the issue identifier is in the list of issues to skip 222, as indicated by block 326 in the flow diagram of FIG. 4, then processing continues at block 328 where issue detection engine 162 determines whether there is more code to scan in code 130 and/or other information to scan in other sources. However, if, at block 326, it is determined that the issue identifier is not in the list of issues to skip 222, then issue detection engine 162 determines whether the status of the corresponding issue should be checked (for instance, based upon the time since the status was last checked), as indicated by block 330. …….. However, if at block 330 it is determined that the status of this issue should be checked, then status detection engine 164 accesses the issue tracking system 140 that is tracking this issue to check on the status of the identified issue, as indicated by block 332 in the flow diagram on FIG. 4”.
Above there are issues to skip (not critical) and issues to consider (maybe critical) but at least are not skipped/ignored. A natural language processor or an AI response processor can identify pieces of code in the issue-related data that appear to be possible or suggested workarounds that may be implemented to address the identified issue.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4, 6-8, 12, 14, 16-17, 20-21 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Rieken et al US20250117195A1 in view of Hawker et al US20240385942A1.
As per claim 1, Rieken discloses a system comprising:
a microprocessor; and a computer readable medium, coupled with the microprocessor and comprising microprocessor readable and executable instructions that, when executed by the microprocessor:
[0079] An example system (FIG. 1, 102A-102M, 106A-106N; FIG. 5, 500; FIG. 7, 700) comprises a processor system (FIG. 7, 702) and a memory (FIG. 7, 704, 708, 710) that stores computer-executable instructions. The computer-executable instructions are executable by the processor system to, based at least on code (FIG. 5, 538) being developed in a developer tool, provide (FIG. 2, 202) an interface element in a user interface (FIG. 5, 556) of the developer tool”.
cause the microprocessor to capture a plurality of sets input parameters:
[0035]” In an aspect, the control logic 514 automatically causes the artificial intelligence model 516 to perform the modification by providing the code modification AI prompt 560 together with the snippet as inputs to the artificial intelligence model 516”.
wherein the captured plurality of sets of input parameters are input into a first Artificial Intelligence (AI) algorithm that generates a plurality of corresponding AI generated source code:
[0035]“In accordance with this aspect, the snippet includes context regarding the code modification AI prompt 560. For example, the code modification AI prompt 560 may be included in the AI prompt(s) 540. In another example, the snippet may be included in the snippet(s) 542. In yet another example, the code modification AI prompt 560 may be written by a developer of the code 538. In accordance with this example, the control logic 514 may generate (e.g., automatically generate) a system-generated prompt that includes the snippet based at least on the code modification AI prompt 560. In further accordance with this example, the control logic 514 may provide the code modification AI prompt 560 together with the system-generated prompt, which includes context regarding the code modification AI prompt 560, as inputs to the artificial intelligence model 516”;
wherein each set of the captured plurality of sets of input parameters comprises one or more input parameters:
[0048]” In accordance with this embodiment, the method of flowchart 200 further includes causing the artificial intelligence model to correct the programming error by providing a second artificial intelligence prompt, which specifies that the programming error is to be corrected, together with at least a portion of the code that includes the programming error as second inputs to the artificial intelligence model. The second artificial intelligence prompt may be a system-generated prompt or a prompt that is generated by a developer of the code.
wherein the first new input parameter is used to generate a new corresponding AI generated source code.
[0035]” AI prompt 560 via the interface element, the control logic 514 automatically causes the artificial intelligence model 516 to perform the modification on at least a snippet of the code 538 to provide a modified code snippet, which is included in modified snippet(s) 562. In an aspect, the control logic 514 automatically causes the artificial intelligence model 516 to perform the modification by providing the code modification AI prompt 560 together with the snippet as inputs to the artificial intelligence model 516”;
wherein the user interface is configured to indicate whether the proposed new first input parameter is the same as or similar to the first input parameter:
[0073]” The AI-modified code recommendation logic identifies a programming error in the modified snippet and provides an error indicator 622 in the processed version of the modified snippet 618, which indicates a symbol that is associated with the programming error. For instance, the AI-modified code recommendation logic recognizes that the symbol “_data” has been changed to “data”, and this has resulted in the programming error. By identifying the programming error in the processed version of the modified snippet 618, the AI-modified code recommendation logic enables the developer of the code 602 to see (and potentially correct) the programming error prior to acceptance of the modification”;
receive, via the user interface, the user's approval or disapproval of the proposed new first input parameter:
[0065] At step 406, a determination is made that the modification of the snippet is accepted via the second interface element. In an example implementation, the replacement postponing logic 530 determines that the modification of the snippet is accepted via the second interface element. For example, the replacement postponing logic 530 may make the determination based on receipt of a modification acceptance indicator 570, which indicates that the modification of the snippet is accepted via the second interface element.
in response to the user's approval, modify the first input parameter to the proposed new first input parameter.
[0065 “For instance, generation of the modification acceptance indicator 570 may be triggered by the user providing a user-generated instruction via the second interface element, which indicates that the modification of the snippet is accepted.”
and input the modified first input parameter to the first AI algorithm to generate a new corresponding AI generated source code.
[0066] At step 408, based at least on the modification of the snippet being accepted via the second interface element, the replacement of the snippet in the code with the modified snippet is triggered to provide updated code. In an example implementation, the first snippet replacement logic 524 triggers the replacement of the snippet in the code 538 with the modified snippet to provide updated code 564 based at least on receipt of the triggering instruction 568 (e.g., based at least on the triggering instruction 568 instructing the first snippet replacement logic 524 to replace the snippet in the code 538 with the modified snippet.
But not explicitly:
scan the plurality of corresponding AI generated source code to identify an issue;
identify, using a second AI algorithm, a first input parameter from the plurality of sets of input parameters that is associated with the identified issue:
wherein the first input parameter is identified based on a ranking score.
generate, by the second Al algorithm, a proposed new first input parameter based on the first input parameter.
generate, for display in a user interface, the proposed new first input parameter for a user's approval or disapproval,
Hawker discloses:
scan the plurality of corresponding AI generated source code to identify an issue:
[0045]”It is also assumed that an automated code scanner (e.g., code scanning system 116) is configured to intermittently scan code 130 in order to surface information to enhance issue resolution, as indicated by block 258 in the flow diagram of FIG. 3. It will be noted that, while code scanning system 116 is shown as a separate tool in FIG. 1, system 116 could be deployed within the continuous integration system 112 and triggered when a build operation is performed, or in other ways”;
identify, using a second AI algorithm, a first input parameter from the plurality of sets of input parameters that is associated with the identified issue:
[0047]”Issue detection engine 162 then scans code 130 and/or other sources of information for issues identified by issue identifiers 134, that may have workarounds 136 associated with them. As discussed above, issue identifiers 134 can include webpages, URL, link, etc. Scanning the code and/or other sources of information for issues and workarounds is indicated by block 278 in the flow diagram of FIG. 3.
wherein the first input parameter is identified based on a ranking score:
[0055] “If the issue identifier is in the list of issues to skip 222, as indicated by block 326 in the flow diagram of FIG. 4, then processing continues at block 328 where issue detection engine 162 determines whether there is more code to scan in code 130 and/or other information to scan in other sources. However, if, at block 326, it is determined that the issue identifier is not in the list of issues to skip 222, then issue detection engine 162 determines whether the status of the corresponding issue should be checked (for instance, based upon the time since the status was last checked), as indicated by block 330. …….. However, if at block 330 it is determined that the status of this issue should be checked, then status detection engine 164 accesses the issue tracking system 140 that is tracking this issue to check on the status of the identified issue, as indicated by block 332 in the flow diagram on FIG. 4”.
generate, by the second Al algorithm, a proposed new first input parameter based on the first input parameter.
[0063] “In one example, workaround code identifier 192 identifies different code patterns or code snippets that have been suggested in the extracted data as possible workarounds that can be used to address the identified issue. For example, natural language processor 190 or AI response processor 198 can identify pieces of code in the issue-related data that appear to be possible or suggested workarounds that may be implemented to address the identified issue”;
generate, for display in a user interface, the proposed new first input parameter for a user's approval or disapproval,
[0064] “Suggestion generator 200 can then generate a suggested operation to address the identified workarounds 136. For instance, the suggested operation may be to delete the workarounds 136 from code 130. If automatic deletion of the code is enabled, then suggested automation operation generator 202 can identify on a user interface that the delete operation is a selectable operation that can be selected by developer 120 and automatically performed.
Hawker also discloses:
wherein the user interface is configured to indicate whether the proposed new first input parameter is the same as or similar to the first input parameter:
[0063]”Workaround code matching system 194 can then attempt to match those pieces of code against corresponding pieces of code in code 130 to determine whether any of the potential workarounds have been implemented as workarounds 136 in code 130. When workaround code matching system 194 finds code in code 130 that matches one of the possible workarounds, then workaround code matching system 194 identifies that location in code 130 as a workaround 136 that should be addressed in response to the change in status of the identified issue. Identifying possible workarounds in the issue-related data is indicated by block 370 in the flow diagram of FIG. 5”;
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate the teachings of Hawker into teachings of Rieken to perform an operation to clean up the code in a code base given a change in status. Furthermore, to reduce a laborious and error prone process in which a developer tries to locate issues in the code base, identify the status of those issues to determine whether the status has changed. And finally, to use a code scanning system in the code base to locate issue identifiers that identify issues for which a corresponding workaround has been implemented. The code scanning system automatically identifies the status of each issue to determine whether there has been a status change. If there has been a status change, the code scanning system identifies one or more suggested operations that should be performed in response to the status change and generates an output identifying the issue, the status change, and the suggested operations, for operator interaction. [Hawker 0016].
As per claim 4, the rejection of claim 1 is incorporated and furthermore Rieken and Hawker disclose:
wherein an alternate new input parameter is also displayed in the user interface and wherein the user interface is configured to receive a user selection to replace the first input parameter or the input parameter similar to the identified first input parameter with the alternate new input parameter.
Reiken [0073]”The AI-modified code recommendation logic identifies a programming error in the modified snippet and provides an error indicator 622 in the processed version of the modified snippet 618, which indicates a symbol that is associated with the programming error. For instance, the AI-modified code recommendation logic recognizes that the symbol “_data” has been changed to “data”, and this has resulted in the programming error. By identifying the programming error in the processed version of the modified snippet 618, the AI-modified code recommendation logic enables the developer of the code 602 to see (and potentially correct) the programming error prior to acceptance of the modification”;
As per claim 6, the rejection of claim 1 is incorporated and furthermore Rieken and Hawker disclose:
wherein the identified issue has a corresponding snippet of source code:
Reiken [0048]”In an example error fixing embodiment, the method of flowchart 200 further includes determining that replacement of the snippet in the code with the modified snippet causes the code to include a programming error. “
wherein the corresponding snippet of source code is a second new input parameter provided to the first AI algorithm to generate the new corresponding AI generated source code:
Reiken [0048]“In an example implementation, the error determination logic 534 determines that replacement of the snippet in the code 538 with the modified snippet causes the code 538 to include the programming error. In accordance with this embodiment, the error determination logic 534 generates error information 550 to indicate (e.g., specify or describe) the programming error.”;
and wherein the corresponding snippet of source code is a negative input to the first AI algorithm that causes the first AI algorithm to not generate source code similar to or the same as the corresponding snippet of source code.
Reiken [0048] “In accordance with this embodiment, the method of flowchart 200 further includes causing the artificial intelligence model to correct the programming error by providing a second artificial intelligence prompt, which specifies that the programming error is to be corrected, together with at least a portion of the code that includes the programming error as second inputs to the artificial intelligence model.”
Examiner interpretation:
No need to generate same source code because it includes the error. That’s a negative input to the AI to not generate such code anymore: [Kohisseri [0085]].
As per claim 7 the rejection of claim 6 is incorporated and furthermore Rieken and Hawker disclose:
wherein the corresponding snippet of source code comprises a plurality of corresponding snippets of source code for a plurality of issues identified in the plurality of corresponding AI generated source code.
Reiken [0073] “For instance, the AI-modified code recommendation logic recognizes that the symbol “_data” has been changed to “data”, and this has resulted in the programming error. By identifying the programming error in the processed version of the modified snippet 618, the AI-modified code recommendation logic enables the developer of the code 602 to see (and potentially correct) the programming error prior to acceptance of the modification. It should be noted that debugging functionality of the developer tool, which is available to fix errors in the code 600, is also available to fix errors in the modified snippet.”;
As per claim 8 the rejection of claim 7 is incorporated and furthermore Rieken and Hawker disclose:
wherein the plurality of corresponding snippets of source code are displayed to a user so the user can determine which ones of the plurality of snippets of source code can be used for the second new input parameter:
Rieken [0065] “At step 406, a determination is made that the modification of the snippet is accepted via the second interface element. In an example implementation, the replacement postponing logic 530 determines that the modification of the snippet is accepted via the second interface element. For example, the replacement postponing logic 530 may make the determination based on receipt of a modification acceptance indicator 570, which indicates that the modification of the snippet is accepted via the second interface element
Claims 12, 14, 16, 17 are the method claims corresponding to system claims 1, 4, 6, 7 and rejected under the same rational set forth in connection with the rejection of claims 1, 4, 6, 7 above.
Claims 20, 21 ,23 are the non-transient computer readable medium corresponding to system claims 1, 4, 6 and rejected under the same rational set forth in connection with the rejection of claims 1, 4, 6 above.
Claims 5, 9-11, 15 and 18-19 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Rieken et al US20250117195A1 in view of Hawker et al US20240385942A1 and further in view of Kohisseri et al US20220188079A1
As per claim 5, the rejection of claim 3 is incorporated and furthermore Rieken and Hawker do not explicitly disclose:
wherein an output issue scanner is a Generative Adversarial Network (GAN) discriminator, and the GAN discriminator is used to scan the plurality of corresponding AI generated source code:
and wherein the second AI algorithm is a GAN generator, and wherein the GAN discriminator and the GAN generator comprise a GAN model.
Kohisseri discloses:
wherein an output issue scanner is a Generative Adversarial Network (GAN) discriminator, and the GAN discriminator is used to scan the plurality of corresponding AI generated source code:
[0098]“In an embodiment, the code creation model may use the description from technology websites and its code and/or command representations to learn using Generative adversarial networks (GAN) representations.
and wherein the second AI algorithm is a GAN generator, and wherein the GAN discriminator and the GAN generator comprise a GAN model.
[0007]” Subsequently, the instructions cause the processor to generate one or more source codes for the application flow using at least one pre-trained code generation model. The at least one pre-trained code generation model generates the one or more source codes based on the user inputs, and one or more reference source codes retrieved from predetermined code repositories”;
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate the teachings of Kohisseri into teachings of Rieken and Hawker for receiving user inputs related to requirements of an application from a user and identifying an application flow corresponding to the application by processing the user inputs. Further, the method comprises generating one or more source codes for the application flow using at least one pre-trained code generation model. The at least one pre-trained code generation model generates the one or more source codes based on the user inputs, and one or more reference source codes retrieved from predetermined code repositories. Upon generating the one or more source codes, the method comprises determining one or more best-fit source codes for the application based on similarities among each of the one or more source codes. [Kohisseri 0006].
As per claim 9 the rejection of claim 1 is incorporated and furthermore Reiken and Hawker do not explicitly disclose:
wherein a snippet of the identified first issue is added to a training set of the first AI algorithm and wherein the first AI algorithm is retrained using the snippet of the identified first issue as a negative input for training the first AI algorithm:
Kohisseri discloses:
wherein a snippet of the identified first issue is added to a training set of the first AI algorithm and wherein the first AI algorithm is retrained using the snippet of the identified first issue as a negative input for training the first AI algorithm:
[0098]”In an embodiment, the technology learning model (or an information portal code) may use the one or more source codes 213 and the descriptions of the one or more source codes 213 to train the technology learning model and to learn each code and its representations. In an embodiment, the issue resolution model may use code snippets corresponding to various issue description for learning the code and its representations.
[0096] “In an embodiment, the source code generator 105 may collect the user feedback indicating a complete acceptance, partial acceptance or rejection of the executable source code 109 by the user 101, and use the collected user feedback for training a recommendation model used for validating the one or more best-fit source codes.
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate the teachings of Kohisseri into teachings of Rieken and Hawker for receiving user inputs related to requirements of an application from a user and identifying an application flow corresponding to the application by processing the user inputs. Further, the method comprises generating one or more source codes for the application flow using at least one pre-trained code generation model. At least one pre-trained code generation model generates one or more source codes based on the user inputs, and one or more reference source codes retrieved from predetermined code repositories. Upon generating the one or more source codes, the method comprises determining one or more best-fit source codes for the application based on similarities among each of the one or more source codes.[Kohisseri 0006].
As per claim 10 the rejection of claim 1 is incorporated and furthermore Rieken and Hawker do not explicitly disclose:
wherein a user can select one of the plurality of corresponding AI generated source code based on a ranking and/or a number of issues in each of the plurality of corresponding AI generated source code:
Kohisseri discloses:
wherein a user can select one of the plurality of corresponding AI generated source code based on a ranking and/or a number of issues in each of the plurality of corresponding AI generated source code:
[0094] In an embodiment, selection of the one or more source codes 213 in the one or more clusters may be performed based on at least one of a ranking associated with each of the one or more source codes 213 or a level of match between results obtained by compiling the one or more source codes 213 and results expected by the user 101.
[0096] In an embodiment, the one or more best-fit source codes may be validated based on weightages associated with each of the one or more best-fit source codes. As an example, the weightages for each of the one or more best-fit source codes may be computed based on number of times that each of the one or more best-fit source codes are previously accepted by the user 101.
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate the teachings of Kohisseri into teachings of Rieken and Hawker for receiving user inputs related to requirements of an application from a user and identifying an application flow corresponding to the application by processing the user inputs. Further, the method comprises generating one or more source codes for the application flow using at least one pre-trained code generation model. The at least one pre-trained code generation model generates the one or more source codes based on the user inputs, and one or more reference source codes retrieved from predetermined code repositories. Upon generating the one or more source codes, the method comprises determining one or more best-fit source codes for the application based on similarities among each of the one or more source codes. [Kohisseri 0006].
As per claim 11 the rejection of claim 1 is incorporated and furthermore Rieken and Hawker do not explicitly disclose:
wherein the first input parameter further comprises a snippet of source code that is identified in a training set used to train the first AI algorithm and wherein the snippet of source code is used as a negative input into the first AI algorithm:
Kohisseri discloses:
wherein the first input parameter further comprises a snippet of source code that is identified in a training set used to train the first AI algorithm and wherein the snippet of source code is used as a negative input into the first AI algorithm:
[0084] In an embodiment, after recommending the executable source code 109 to the user 101, the source code generator 105 may collect reviews and feedback from the user 101 on the presented executable source code 109. As an example, the user feedback may be at least one that the user 101 has “fully accepted”, “partially accepted” or “not accepted” the given executable source code 109. In case, the user 101 has “fully accepted” the given executable source code 109, the selected executable source code 109 may be fed back to the pre-trained code generation model 106 as an input to reinforcement learning of the pre-trained code generation model 106. Also, the pre-trained code generation model 106 may consider this instance as a “reward” and learn to recommend similar executable source codes 109 for similar application requirements.
[0085] “Similar learning strategy is followed when the user feedback is “not accepted”.”;
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate the teachings of Kohisseri into teachings of Rieken and Hawker for receiving user inputs related to requirements of an application from a user and identifying an application flow corresponding to the application by processing the user inputs. Further, the method comprises generating one or more source codes for the application flow using at least one pre-trained code generation model. At least one pre-trained code generation model generates one or more source codes based on the user inputs, and one or more reference source codes retrieved from predetermined code repositories. Upon generating the one or more source codes, the method comprises determining one or more best-fit source codes for the application based on similarities among each of the one or more source codes. [Kohisseri 0006].
Claims 15, 18, 19 are the method claims corresponding to system claims 5, 9, 11 and rejected under the same rational set forth in connection with the rejection of claims 5, 9, 11 above.
Claim 22 is the non-transient computer readable medium to system claim 5 and rejected under the same rational set forth in connection with the rejection of claim 5 above.
Pertinent arts:
US 20250117201 A1:
A trust score for each code module of the plurality of code modules is determined, wherein the trust score includes a first trust score component for issue identification and a second trust score component for issue remediation, and wherein the trust score is based on a source of each code module selected from a group of a human-generated source and an artificial intelligence model-generated source.
US 20200272435 A1:
generating a computer program using artificial intelligence module includes generating logic programming by analyzing natural language in sample input data received from an external source.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Contact Information
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
/BRAHIM BOURZIK/ Examiner, Art Unit 2191
/WEI Y MUI/ Supervisory Patent Examiner, Art Unit 2191
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